Personalized intelligent clothes matching recommendation method combined with knowledge graph

Abstract

The invention relates to the technical field of recommendation systems, in particular to a personalized intelligent clothes matching recommendation method in combination with a knowledge graph, which comprises the following steps: acquiring clothes commodity information and user clothing interaction information of an e-commerce platform, and constructing the knowledge graph; modeling the knowledge graph, and learning each entity and relationship in the knowledge graph to obtain an entity vector and a relationship vector; combining the entity vector and the relationship vector with a hybrid recommendation system, and performing score prediction on the single clothing to obtain a clothes single recommendation result; and calculating a matching index of every two clothes single items, automatically matching the two clothes single items with high matching indexes, performing score prediction, and performing a final TOP-N suit recommendation result according to scores. Online clothes suit recommendation is more intelligent.

Description

technical field [0001] The invention relates to the technical field of recommendation systems, in particular to a personalized intelligent clothing collocation recommendation method combined with knowledge graphs. Background technique [0002] With the rapid development of mobile Internet and the wide application of e-commerce, various e-commerce platforms have emerged one after another, such as Taobao, Jingdong, Tmall, Vipshop, etc. However, with the development of the information age, most websites are facing the problems of information overload and information lost, so to address these two problems, the recommendation system came into being. [0003] Among them, the recommendation system also plays an important role on various Internet platforms. Through the recommendation system, users can more efficiently and quickly find products or information that they are interested in or have potential interests in. Based on the user's historical behavior information, the recommen...

AI Technical Summary

Problems solved by technology

However, there are still a series of problems in the traditional recommendation system, such as low recommendation accuracy, unexplainable recommendation, data sparsity and cold start problems, etc.

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